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Our paper takes as its starting point the recent proposal, at the core of this special issue, to use the active inference framework (AIF) to computationally model what it is for a person to live a meaningful life. In broad brushstrokes, the AIF takes experiences of human flourishing to be the result of predictions and uncertainty estimations along many dimensions at multiple levels of neurobiological organization. Our aim in this paper is to explain how AIF models predict that uncertainty can sometimes, under the right conditions, be conducive to the experiences of flourishing. Our focus is on playfulness, because playful individuals have learned a high-level prior that in certain safe contexts, uncertainty and error should be tolerated and explored. They have expanded the phenotypic bound on the amount of surprise they are prepared to tolerate in their lives. The positive embracing of uncertainty has a number of positive knock-on effects for the kind of lives playful individuals are able to lead. First, a playful individual attends to the world in a way that is open and expansive, a mode of attending that is effortless and therefore conducive to being in the present. This openness to the present moment allows for deep engagement and participation in experience that can furnish a renewed appreciation for life. Second, playful individuals will actively seek out spaces at the edge of their own abilities and will therefore be more likely to grow and develop in their skills and relationships in ways that contribute to their living a good life. Finally, playful agents seek out situations in which they can monitor, observe, and learn from their own affective responses to uncertainty. Thus, uncertainty becomes something familiar to them that they not only learn to tolerate but also enjoy positively exploring, in ways that provide them opportunities to grow. For these three reasons, we will argue that playfulness and openness to experiences of uncertainty and the unknown may be important ingredients in human flourishing. [ABSTRACT FROM AUTHOR]

A significant number of persons engage in paradoxical behaviors, such as extreme food restriction (up to starvation) and non-suicidal self-injuries, especially during periods of rapid changes, such as adolescence. Here, we contextualize these and related paradoxical behavior within an active inference view of brain functions, which assumes that the brain forms predictive models of bodily variables, emotional experiences, and the embodied self and continuously strives to reduce the uncertainty of such models. We propose that not only in conditions of excessive or prolonged uncertainty, such as in clinical conditions, but also during pivotal periods of developmental transition, paradoxical behaviors might emerge as maladaptive strategies to reduce uncertainty—by "acting on the body"— soliciting salient perceptual and interoceptive sensations, such as pain or excessive levels of hunger. Although such strategies are maladaptive and run against our basic homeostatic imperatives, they might be functional not only to provide some short-term reward (e.g. relief from emotional distress)—as previously proposed—but also to reduce uncertainty and possibly to restore a coherent model of one's bodily experience and the self, affording greater confidence in who we are and what course of actions we should pursue. [ABSTRACT FROM AUTHOR]

What is it about our current digital technologies that seemingly makes it difficult for users to attend to what matters to them? According to the dominant narrative in the literature on the "attention economy," a user's lack of attention is due to the large amounts of information available in their everyday environments. I will argue that information-abundance fails to account for some of the central manifestations of distraction, such as sudden urges to check a particular information-source in the absence of perceptual information. I will use active inference, and in particular models of action selection based on the minimization of expected free energy, to develop an alternative answer to the question about what makes it difficult to attend. Besides obvious adversarial forms of inference, in which algorithms build up models of users in order to keep them scrolling, I will show that active inference provides the tools to identify a number of problematic structural features of current digital technologies: they contain limitless sources of novelty, they can be navigated by very simple and effortless motor movements, and they offer their action possibilities everywhere and anytime independent of place or context. Moreover, recent models of motivated control show an intricate interplay between motivation and control that can explain sudden transitions in motivational state and the consequent alteration of the salience of actions. I conclude, therefore, that the challenges users encounter when engaging with digital technologies are less about information overload or inviting content, but more about the continuous availability of easily available possibilities for action. [ABSTRACT FROM AUTHOR]

Time plays a significant role in science and everyday life. Despite being experienced as a continuous flow, computational models of consciousness are typically restricted to a sequential temporal structure. This difference poses a serious challenge for computational phenomenology—a novel field combining phenomenology and computational modelling. By analysing the temporal structure of the active inference framework, we show that an integrated continuity of time can be achieved by merging Husserlian temporality with a sequential order of time. We also show that a Markov blanket of the present moment integrates past and future moments of both subjective temporality and objective time in an asynchronous manner. By applying the integrated continuity, it is clear that active inference makes use of both subjective temporality and objective time in an integrated fashion. We conclude that active inference, on a temporal note, qualifies as a computational model for phenomenological investigations. [ABSTRACT FROM AUTHOR]

Self-esteem, the evaluation of one's own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer or an inferential capacity to interpret one's standing within a social group. This approach allows us to explore the interaction between an individual's self-perception and the expectations of their social environment.When there is a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation with recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health., (© The Author(s) 2024. Published by Oxford University Press.)

Here we build on recent findings which show that greater alignment between our subjective experiences (how we feel) and physiological states (measurable changes in our body) plays a pivotal role in the overall psychological well-being. Specifically, we propose that the alignment or 'coherence' between affective arousal (e.g. how excited we 'feel') and autonomic arousal (e.g. heart rate or pupil dilation) may be key for maintaining up-to-date uncertainty representations in dynamic environments. Drawing on recent advances in interoceptive and affective inference, we also propose that arousal coherence reflects interoceptive integration, facilitates adaptive belief updating, and impacts our capacity to adapt to changes in uncertainty, with downstream consequences to well-being. We also highlight the role of meta-awareness of arousal, a third level of inference, which may permit conscious awareness, learning about, and intentional regulation of lower-order sources of arousal. Practices emphasizing meta-awareness of arousal (like meditation) may therefore elicit some of their known benefits via improved arousal coherence. We suggest that arousal coherence is also likely to be associated with markers of adaptive functioning (like emotional awareness and self-regulatory capacities) and discuss mind-body practices that may increase coherence., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a 'generative model' of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent's generative model and of arresting the model's ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest-based on the active inference framework-some ways to understand why some forms of digital technology pose these risks, while others do not., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Distributed processing that gives rise to pain experience is anchored by a multidimensional self-model. I show how the phenomenon of pain asymbolia and other atypical pain-related conditions (Insensitivity to Pain, Chronic Pain, 'Social' Pain, Insensitivity to Pain, Chronic Pain, 'Social' Pain, empathy for pain and suffering) can be explained by this idea. It also explains the patterns of association and dissociation among neural correlates without importing strong modular assumptions. It treats pain processing as a species of allostatic active inference in which the mind co-ordinates its processing resources to optimize basic bodily functioning at different time scales. The self is inferred to be source and target of regulation in this process. The self-modelling account reconciles conflicting deaffectualization and depersonalization accounts of pain asymbolia by showing how depersonalization and pain asymbolia arise at different levels of hierarchical self modelling., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Our paper takes as its starting point the recent proposal, at the core of this special issue, to use the active inference framework (AIF) to computationally model what it is for a person to live a meaningful life. In broad brushstrokes, the AIF takes experiences of human flourishing to be the result of predictions and uncertainty estimations along many dimensions at multiple levels of neurobiological organization. Our aim in this paper is to explain how AIF models predict that uncertainty can sometimes, under the right conditions, be conducive to the experiences of flourishing. Our focus is on playfulness, because playful individuals have learned a high-level prior that in certain safe contexts, uncertainty and error should be tolerated and explored. They have expanded the phenotypic bound on the amount of surprise they are prepared to tolerate in their lives. The positive embracing of uncertainty has a number of positive knock-on effects for the kind of lives playful individuals are able to lead. First, a playful individual attends to the world in a way that is open and expansive, a mode of attending that is effortless and therefore conducive to being in the present. This openness to the present moment allows for deep engagement and participation in experience that can furnish a renewed appreciation for life. Second, playful individuals will actively seek out spaces at the edge of their own abilities and will therefore be more likely to grow and develop in their skills and relationships in ways that contribute to their living a good life. Finally, playful agents seek out situations in which they can monitor, observe, and learn from their own affective responses to uncertainty. Thus, uncertainty becomes something familiar to them that they not only learn to tolerate but also enjoy positively exploring, in ways that provide them opportunities to grow. For these three reasons, we will argue that playfulness and openness to experiences of uncertainty and the unknown may be important ingredients in human flourishing., Competing Interests: The authors confirm that there are no conflicts of interest in the preparation and writing of this article., (© The Author(s) 2023. Published by Oxford University Press.)

The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience-a Minimal Unifying Model of consciousness., (© The Author(s) 2021. Published by Oxford University Press.)

Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by 'subjective valuation'-a deep inference about the precision or 'predictability' of the self-evidencing ('fitness-promoting') outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of 'selflessness'; in particular the 'totally selfless' states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology., (© The Author(s) 2021. Published by Oxford University Press.)

Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data., (© The Author(s) 2021. Published by Oxford University Press.)

Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our "minimal physicalist" approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems., (© The Author(s) 2021. Published by Oxford University Press.)

It is well known that the human brain continuously predicts the sensory consequences of its own body movements, which typically results in sensory attenuation. Yet, the extent and exact mechanisms underlying sensory attenuation are still debated. To explore this issue, we asked participants to decide which of two visual stimuli was of higher contrast in a virtual reality situation where one of the stimuli could appear behind the participants' invisible moving hand or not. Over two experiments, we measured the effects of such "virtual occlusion" on first-order sensitivity and on metacognitive monitoring. Our findings show that self-generated hand movements reduced the apparent contrast of the stimulus. This result can be explained by the active inference theory. Moreover, sensory attenuation seemed to affect only first-order sensitivity and not (second-order) metacognitive judgments of confidence. [ABSTRACT FROM AUTHOR]

Self-generated movement leads to the attenuation of predicted sensory consequences of the movement. This mechanism ensures that attention is generally not drawn to sensory signals caused by own movement. Such attenuation has been observed across the animal kingdom and in different sensory modalities. In this study we used novel virtual reality (VR) devices to test the hypothesis that the human brain attenuates visual sensation in the area of the visual field where the subject's hand is currently moving. We conducted three VR experiments where we monitored hand position during movement while the participants performed a visual search task. In the first two experiments we measured response time for salient moving targets and observed that reaction time (RT) is slower for targets that are behind the (invisible) hand. This result provides the first evidence that the visual motion signals generated by the subject's own hand movement are suppressed. In the third experiment we observed that RT is also slower for colored targets behind the hand. Our findings provide support for the active inference account of sensory attenuation, which posits that attenuation occurs because attention is withdrawn from the sensory consequences of own movement. Furthermore, we demonstrate how modern VR tools could open up new exciting avenues of research for studying the interplay of action and perception.